{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding the rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make dummy variables for rank\n",
    "one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)\n",
    "\n",
    "# Drop the previous rank column\n",
    "one_hot_data = one_hot_data.drop('rank', axis=1)\n",
    "\n",
    "# Print the first 10 rows of our data\n",
    "one_hot_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Copying our data\n",
    "processed_data = one_hot_data[:]\n",
    "\n",
    "# Scaling the columns\n",
    "processed_data['gre'] = processed_data['gre']/800\n",
    "processed_data['gpa'] = processed_data['gpa']/4.0\n",
    "processed_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backpropagating the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def error_term_formula(x, y, output):\n",
    "    return (y - output)*(sigmoid_prime(x))"
   ]
  }
 ],
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